MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
arXiv:2011.05088 · doi:10.1109/LGRS.2021.3079925
Abstract
There are limited studies on the semantic segmentation of high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images due to the scarcity of training data and the inference of speckle noises. The Gaofen contest has provided open access of a high-quality PolSAR semantic segmentation dataset. Taking this chance, we propose a Multi-path ResNet (MP-ResNet) architecture for the semantic segmentation of high-resolution PolSAR images. Compared to conventional U-shape encoder-decoder convolutional neural network (CNN) architectures, the MP-ResNet learns semantic context with its parallel multi-scale branches, which greatly enlarges its valid receptive fields and improves the embedding of local discriminative features. In addition, MP-ResNet adopts a multi-level feature fusion design in its decoder to make the best use of the features learned from its different branches. Ablation studies show that the MPResNet has significant advantages over its baseline method (FCN with ResNet34). It also surpasses several classic state-of-the-art methods in terms of overall accuracy (OA), mean F1 and fwIoU, whereas its computational costs are not much increased. This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images. The code is available at: https://github.com/ggsDing/SARSeg.
References in corpus (4)
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
- LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- Improving Semantic Segmentation of Aerial Images Using Patch-based Attention
- HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data